In the last twenty years, fundamental goals of engine manufacturers are to achieve significant reductions of the amounts of pollutants emitted at the engine exhaust, and lower fuel consumption without compromising speed and torque performances. For these reasons, an efficient engine control based on a comprehensive monitoring of the many engine working parameters is desired.
To maintain a strict control of the engine working parameters, Engine Management Systems (EMS) or Engine Control Units (ECU) are used. The EMS implements control strategies which achieve the optimum trade-off between several contradictory objectives: high output power when required by the driver, low emission levels and low fuel consumption. At the same time, in a spark-ignition engine, the EMS brings and maintains the engine in a specified operating range such that the three-way catalytic converter can further reduce the undesired content of the exhaust gases. The EMS controls the amount of fuel injected in the engine combustion chamber (fuel pulse width), the point in the engine cycle at which the mixture air fuel is ignited (ignition timing) and in advanced engine designs, other parameters, such as the valve timing. The EMS determines values for these parameters from measured quantities such as speed, torque, air mass flow rate, inlet-manifold pressure, temperatures at several critical points and throttle angle.
FIG. 1 illustrates the EMS function. The EMS determines values for Controlled Variables from knowledge of the Measured Variables to achieve the System Aims. EMS essentially includes three components: engine maps (look-up tables stored in a ROM), controller and sensors, as schematically depicted in FIG. 2.
In addition to sensors for measuring quantities of interest, such as speed, manifold pressure, air mass flow rate, temperature (that is, the Measured Variables appearing in both FIGS. 1 and 2), in FIG. 2 appear other sensors too. These additional devices monitor whether the engine is working according to the System Aims, or not. Therefore, they have an active part in the real time updating process of controlled variables and, eventually of the engine maps. For example, in a spark-ignition engine a sensor of this type is the so-called lambda sensor. The lambda sensor, mounted in the exhaust stream as schematically shown in the block diagram of FIG. 3, determines whether the lambda ratio (that is AFR/AFRstoichiometric) is above or below unity from the amount of oxygen detected in the exhaust gas mixture. The EMS uses this information to adjust the fuel pulse width and/or the ignition timing to keep the lambda ratio as close as possible to unity.
To keep the air/fuel ratio (AFR) within such a narrow range, a lambda sensor is inserted in the outlet of exhaust gases for monitoring the amount of oxygen in the exhaust gases. The lambda sensor provides a signal representative of the value of the ratio
  λ  =            Air      ⁢              /            ⁢      Fuel              Air      ⁢              /            ⁢              Fuel        stoichiometric            If λ<1 the mixture is rich of fuel, while if λ>1 the mixture is lean of fuel, as schematically shown in FIG. 4.
The signal generated by the lambda sensor is input to the controller of the engine that adjusts the injection times and thus the fuel injected during each cycle for reaching the condition λ=1.
Many lambda sensors actually available, the so-called on/off lambda sensors, do not evaluate the ratio of the mixture and thus the exact value of λ, but signal whether the mixture is reach or lean. Considering that the injection time should ideally be proportional to the air/fuel ratio, these on/off lambda sensors do not allow a precise regulation.
There are lambda sensors that generate a signal representative of the effective value of the air/fuel ratio, but these lambda sensors (the so-called “wide-band lambda sensors”) are either very expensive or not very accurate. The following table compares costs and accuracies of commercially available “wide-band lambda sensors”:
accuracyaccuracy foraccuracyfor leanstoichiometricfor richcostmixturesmixturesmixtures(USD)McLaren1.7%0.1%1.7%1500-1800electronicsystemsMoTeC2.5%1.75% 1.75% 800-900Bosch LSM1.5%unknownunknown300-40011Horiba LD-8.0%4.0%8.0%60-80700
Engines manufacturers are generally reluctant to a proliferation of sensors unless they produce valuable improvements that could not otherwise be attained. Virtual-sensors techniques are generally welcome because of their comparably lower cost, reliability and sturdiness. Virtual-sensors allow estimates of quantities of interest without the necessity for sensors dedicated to the measurements. In this field, intelligent systems models, such as neural networks, are attractive because of their capabilities in pattern recognition and signal analysis problems [1].
An approach to realize a virtual lambda sensor uses neural networks to correlate certain features of spark plug voltage waveforms with specific values of air fuel ratio [2], [3]. The spark plug is in direct contact with the combustion processes which are occurring in the engine cylinder, hence analysis of the spark plug voltage waveforms seems to be potentially a suitable method of monitoring combustion in spark ignition engines.
There are essentially two methods of using a spark plug as a combustion sensor, namely: the Ionic-Current and Spark Voltage Characterization (SVC) methods: In the ionic-current system, the spark plug is used as a sensor during the “non firing” phase of the pressure cycle, which is the part of the pressure cycle after the spark advance, that is, after the spark ignition. This is done by applying a small voltage of about 100 Volts to the spark plug and measuring the current. The current is supported by reactive ions in the flame that carry on ionic current across the spark plug gap. The type and the number of ions, formed during and after the combustion, depends on the combustion conditions. The Ionic-Current depends also on other parameters such as temperature, pressure and other. Recently, much work has been done on the use of Ionic-Current for monitoring combustion [4], [5], [6] [7].
The SVC method rests on the analysis of the time-varying voltage detected across the gap of the spark plug. Since the SVC method involves the analysis of the ignition voltage waveform itself, it does not require additional biasing means and associated high voltage switching circuitry.
FIG. 5 illustrates a typical spark voltage waveform. The shape of spark voltage waveform has several predictable phases. When the EHT (Extra High Tension) pulse is generated, the potential difference across the gap rises up to 12 kV and breakdown occurs. Breakdown is a fall in voltage that produces a characteristic voltage spike of about 10 μs in duration. Thereafter, a glow-discharge tail region of the waveform of a few milliseconds duration appears. Tests have demonstrated that changes of engine working parameters lead to changes of the shape of certain features of the waveform. However, it is far from being, easy to predict these variations as the engine parameters are varied. In fact, random variations occur between successive sparks even when engine working parameters are kept constant.
Interactions of parameters, such as combustion temperatures, compression, composition of the air-fuel gas mixture, affect the shape of the breakdown voltage spike in the spark voltage waveform. Changes of the lambda ratio lead to breakdown voltage changes and to subtle changes in the overall shape of the ignition spark waveform. Lambda ratio changes appear to affect both the shapes of the breakdown voltage spike and of the flow-discharge tail portion of the waveform. An analytic relationship between lambda values and instantaneous voltage values of the spark voltage waveforms has not been found yet. However, several articles ([8] and [9]) sustain a correlation between the vector formed through a periodic sampling of the spark plug voltage (spark-voltage vector) and lambda values.
The Spark Voltage Characterization (SVC) technique is based on setting up an effective neural network for associating the spark-voltage vector and lambda ratio.
AFR Estimation Using Spark Voltage Characterization by Neural Network
According to R. J. Howlett et al. in [8], [9], and [10] it is possible to design a Virtual Lambda Sensor, that is a device for sensing the air/fuel without analyzing the exhaust gases of the engine.
Such a virtual sensor is based on a neural network trained to find the best correlation between characteristic aspects of the spark voltage waveform and lambda values. The trained neural network determines, for a current vector of characteristic values of the spark voltage, whether the air/fuel ratio (lambda value) is in the stoichiometric mixture range or in lean or rich mixture ranges.
FIG. 6 shows a typical experimental arrangement to acquire data for training of virtual lambda sensor models. The dynamometer, by which an engine “dummy” load may be varied as desired, is used to measure load-torque and to calculate the output power. Setting of throttle position and fuel pulse width allows changing the air-fuel ratio. In this way, a data set related to the whole range of lambda values may be established.
The blocks EMU, A-D converter and DSP are an Engine Management Unit, Analog-to-Digital converter and Digital Signal Processor, respectively.
Air-fuel ratio values are measured by an exhaust gas analyzer. To measure spark plug voltage the ignition system is modified by the addition of a high-voltage test-probe at the spark plug.
In these approaches, a MLP (Multiple Layer Perceptron) neural network, with a single hidden layer unit and sigmoidal activation unit, is used as a spark-voltage vector classifier.
In a supervised training paradigm, a back-propagation learning algorithm sets the MLP training. The training file contains Nt pairs input-output; model input is an instantaneous spark-voltage vector of the form Vi=(v1, v2, . . . , vm), with i=1, . . . , Nt and m equal to the length of the spark-voltage vector; model output is a desired output vector of the form Dr=(0,0,1), Dstoi=(0,1,0) and Dl=(1,0,0), depending on whether the lambda value, associated to the current spark-voltage vector, is rich (<1), stoichiometric (≈1) or lean (>1).
Three sets of spark-voltage vectors and their associated desired output vectors build the training file. Similar files, built by data not to be used for training, are created for validation and test purposes. In this case, during the testing phase, to estimate the model forecast capability it is sufficient to count the number of times in which model output doesn't match the desired output value. The ratio between this number and the total number of estimates represents the model classification error. An alternative quantity for describing the model forecast capability can be simply obtained as difference between 1 and the classification error. This alternative quantity is usually called correct classification rate.
R. J. Howlett et al. [8], [9] carried out a multi-speed test with the same 92 cc single-cylinder four-stroke engine. In this case, they used a more closely-spaced range of lambda values, i.e. 0.9, 1.0 and 1.1. FIG. 7 shows the trend of the correct classification rate of the virtual lambda sensor model versus engine speed for various model training file sizes. The normalized size of the training file σ, used in this test, satisfies the following relationship Nc=Nwσ, where Nt is the size of training file and Nw is the number of weights of the MLP neural network modeling virtual lambda sensor.
These approaches have important drawbacks. The above virtual lambda sensors are unable to indicate the actual AFR but only if the AFR is in one or the other range. In other words, they cannot confirm lambda values approximately equal to 0.95 or 1.05 as illustrated by the rectangles in FIG. 8.
The number of cycles of integration, according to the approach aimed at reducing the effect of random variations observed in successive spark waveforms, is not specified. However, this would be an important parameter when realizing a fast gasoline engine injection control system.
The forecast capability of the system of R. J. Howlett et al. [8-9] has a strong dependence on engine speed.
It has been shown [20] that at an MBT condition (Maximum spark advance, evaluated in respect to the TDC for the Best Torque) the pressure peak in a cylinder during combustion is correlated with the air/fuel ratio, while the location of the pressure peak at a fixed air/fuel ratio value is correlated with the spark advance. Therefore, it is possible to regulate the air/fuel ratio at stoichiometric conditions simply by correcting the fuel injection to keep constant the position of the crank at which the pressure peak is attained, and keeping the pressure peak at a certain value.
The so-called MBT condition is the operating condition of the engine when the spark advance takes on the maximum value before bringing the engine toward the knocking phenomena. Normally, this condition is not often verified during the functioning of the engine.
In [20], a neural network for sensing the position of the crank when the pressure peak occurs (that is the Location of the Pressure Peak, or briefly the LPP parameter) and the pressure peak value (briefly, the PP parameter) is also disclosed. This neural network is embodied in an air/fuel ratio feedback regulator, and provides to a control system of the engine, signals representing the LPP and the PP parameters. This control system drives the motor in order to keep constant the LPP parameter and to keep constant the air/fuel ratio by regulating the pressure peak in the cylinders.
Unfortunately, this document though establishing that there is only a relationship between the air/fuel ratio and the pressure peak if the LPP parameter of the motor is constant (in particular, if the LPP parameter corresponds to the value for MBT condition), is silent about any possibility of assessing the actual air/fuel ratio as a function of the pressure peak without employing a classic lambda sensor under any condition of operation of the engine.
As a matter of fact, the correlation between the pressure peak and the air/fuel ratio has been demonstrated only in steady-states at certain operating conditions, that is, at MBT conditions, at 2000 rpm and MAP of 0.5 and 0.8 bar.
The system disclosed in that document does not lend itself for sensing the air/fuel ratio, that is for generating a signal that represents at each instant the current value of the air/fuel ratio of the engine.
Therefore, the need remains for a low cost manner of sensing the air/fuel ratio with a sufficient accuracy under any condition of operation of the engine.